Method and system for model-based design and layout of an integrated circuit

ABSTRACT

A approach is described for allowing electronic design, verification, and optimization tools to implement very efficient approaches to allow the tools to directly address the effects of manufacturing processes, e.g., to identify and prevent problems caused by lithography processing. Fast models and pattern checking are employed to integrate lithography and manufacturing aware processes within EDA tools such as routers.

CROSS REFERENCE TO RELATED APPLICATION(S)

This application is a divisional application of U.S. patent applicationSer. No. 12/133,563, filed on Jun. 5, 2008 and entitled “METHOD ANDSYSTEM FOR MODEL-BASED DESIGN AND LAYOUT OF AN INTEGRATED CIRCUIT”. Thisapplication is also cross related to U.S. patent application Ser. No.13/535,225, filed concurrently under Atty. Dkt. No. 07PA074US01D01 withthe instant application and entitled “METHOD AND SYSTEM FOR MODEL-BASEDDESIGN AND LAYOUT OF AN INTEGRATED CIRCUIT”. The content of both U.S.patent applications is hereby expressly incorporated by reference forits entirety for all purposes.

BACKGROUND AND SUMMARY

The invention is directed to a more efficient approach for hotspotdetection, for implementing layout, and for placement, routing, andverification of integrated circuit designs.

A semiconductor integrated circuit (IC) has a large number of electroniccomponents, such as transistors, logic gates, diodes, wires, etc., thatare fabricated by forming layers of different materials and of differentgeometric shapes on various regions of a silicon wafer.

The various components of an integrated circuit are initially defined bytheir functional operations and relevant inputs and outputs. From theHDL or other high level description, the actual logic cellimplementation is typically determined by logic synthesis, whichconverts the functional description of the circuit into a specificcircuit implementation.

An integrated circuit designer may use a set of EDA application programsto create a physical integrated circuit design layout from a logicalcircuit design. The EDA application uses geometric shapes of differentmaterials to create the various electrical components on an integratedcircuit and to represent electronic and circuit IC components asgeometric objects with varying shapes and sizes. During this process,the design components are “placed” (i.e., given specific coordinatelocations in the circuit layout) and “routed” (i.e., wired or connectedtogether according to the designer's circuit definitions).

After an integrated circuit designer has created the circuit layout,verification and/or optimization operations are performed on theintegrated circuit layout using a set of EDA testing and analysis tools.These actions are performed since significant variations from theas-designed IC product may occur to the as-manufactured IC product dueto the optical and/or chemical nature of the processing used tomanufacture the integrated circuit. For example, optical distortionsduring the lithography process may cause variations in featuredimensions (e.g. line widths) that are patterned using masks. Physicalverification would occur to help identify areas of significant risks forproblematic variations. Design optimization approaches, such as OPC(optical proximity correction) and RET (resolution enhancementtechniques) could be used to create an as-manufactured product that moreclosely matches the configuration of the as-designed layout.

The design rule check (DRC) process has long been used to help minimizemanufacturing problems, by ensuring that the circuit design abides by aset of detailed rules and parameters that the foundry specifies for itsmanufacturing process. Essentially, each rule is associated with one ormore parameter values that are checked for compliance with the rule. TheDRC process will check those parameters to produce a simple “yes” or“no” answer as to whether the rule has been violated. For example, avery common rule is to check for minimum spacing between objects in alayout. DRC processing will determine whether all objects meet theminimum spacing requirements. If all objects meet the spacingrequirements, then the layout meet the rules requirement for spacing. Ifany objects are spaced closer together than the minimum spacingrequirement, then a rules violation will be identified. If there are anyrules violations, then the layout will need to be modified to correctthe rules violation. If no rules violations have been identified, thenthe IC design is passed to the next design stage for manufacturing.

DRC tools typically read and manipulate a design database which storesinformation about device geometries and connectivity. Because compliancewith design rules generally constitutes the gating factor between onestage of the design and the next, DRC tools are typically executedmultiple times during the evolution of the design and contributesignificantly to the project's critical path. Therefore, reducing DRCtool execution time makes a major contribution to the reduction ofoverall design cycle times. In addition, DRC rules often contain designconstraints that are much more limiting than are needed for anyparticular design or portion of a design. DRC rules are often set at the“lowest common denominator” level to ensure that most or all IC designswill properly operate. However, certain IC design may actually needparameters that are more or less cautious than other designs. Since DRCrules typically operate on an “all or nothing” basis, this means thatmany IC design may fail DRC processing even though they would functionproperly for intended purposes if manufactured. Further, manufacturingan IC design has gotten steadily more difficult, so much so that certain2D configurations of geometry may not function properly, even thoughthey satisfy all the DRC rules.

To address this issue, model-based approaches can be used to verify thecircuit design, by using the model to check the design formanufacturing-induced problems. As used herein, the term “model” refersto a set of data that identifies one or more specific characteristicswithin an IC layout and data relating to its effect, manufacturability,and/or usability. A lithography model is a common example of a type ofmodel that is used by EDA tools during many phases of the electronicdesign process, such as physical design, implementation, andverification.

FIG. 1 illustrates one approach for performing model-based verificationand optimization in the context of routing. This approach begins with apre-routed design 102 having a set of geometric circuit elements/shapescreated with a layout tool. A routing tool would then be used forimplementing interconnect elements on the layout to create a routeddesign 104. The conventional router does not have knowledge oflithography issues. As a result, the routed design may contain numerouslayout portions that are problematic once manufacturing is performed,and would contribute to yield or functionality problems.

During the verification/optimization, models (such as lithographymodels) are used by a lithography simulation device 106 to predict theas-manufactured product that would result from processing the layout ina given manufacturing facility and using a given set of processingequipment and parameters. Optimizations, such as RET optimizations, mayalso be performed to increase the likelihood of a manufacturable design.If problems are identified, then the design may return for re-routing tocorrect the identified problems. Numerous iterations of this process mayoccur before a design is finalized. The term RET (reticle enhancementtechnology), as used here, includes the use of optical proximitycorrection (OPC), sub-resolution assist features (SRAF), phase shiftmasks (PSM), etc.

The problem with this approach is that lithography simulation and REToptimization is very resource intensive, requiring large quantities ofboth time and computing assets for adequate results. As the quantity ofdata in modern IC designs become larger and larger over time, theresources required for performing model-based verification andoptimization upon these IC designs also becomes much greater. Thisproblem is exacerbated by constantly improving IC manufacturingtechnologies that can create IC chips at ever-smaller feature sizes,which allows increasingly greater quantities of transistors to be placedwithin the same chip area, resulting in more complex physical andlithographic effects during manufacture.

To address these problems, among others, the present invention in someembodiments provides an approach for allowing EDA tools to implementvery efficient approaches to allow the tools to directly address theeffects of manufacturing processes, e.g., to identify and preventproblems caused by lithography processing. Fast models and patternchecking are employed to integrate lithography and manufacturing awareprocesses within EDA tools such as routers. In some embodiments, thisapproach avoids the rigid requirement of conventional tools that relyexclusively upon rule checks, avoiding the need to create overly complexrules that may or may not accurately reflect the real-worldmanufacturing problems that may occur to the design. This also avoidsthe need to hardcode the EDA tool (e.g., modify the router software) toexplicitly target particular bad patterns, which significantly reducesor eliminates the need to perform this highly manual and error-proneeffort. Moreover, this approach in some embodiments can also be used toavoid the very slow process of using external tools to verify the routeddesign and to provide feedback to the router. In this context, theexternal tool will often call the problems it finds “hotspots”.

BRIEF DESCRIPTION OF FIGURES

FIG. 1 shows a flow of routing processing with lithography simulation.

FIG. 2 shows fast models and pattern matching integrated into a routerflow, according to some embodiments of the invention.

FIG. 3 shows fast models and pattern matching generally used for a chipflow, according to some embodiments of the invention.

FIG. 4 shows an approach for implementing hotspot analysis, according tosome embodiments of the invention.

FIGS. 5, 7, and 9 show a model building flows, according to someembodiments of the invention.

FIGS. 6, 8, and 10 show flows for performing analysis based upon models,according to some embodiments of the invention.

FIGS. 11-12 show approaches to improve routing using pattern matching,according to some embodiments of the invention.

FIG. 13 shows an architecture of an example computing system with whichthe invention may be implemented.

DETAILED DESCRIPTION

The present approach is directed to an improved method, system, andcomputer readable medium for performing routing, full chip flows, and“hotspot” detection. As used herein, the term “hotspot” refers to aportion of a design layout that is identified as corresponding to amanufacturing-related problem, such as may be caused by lithography,chemical metal polishing (CMP), etch, strain, critical area analysis, orany combination thereof. Some embodiments of the invention utilizepattern matching to detect hotspots and to provide lithographicallyaware routing and chip design, by creating and accessing a bad patternlibrary. The term “bad pattern”, according to some embodiments, refersto any layout shape or shape pattern that has a negative impact on yieldor functionality due to, for example, lithography, CMP, strain, criticalarea analysis, and other manufacturing related issues. In this document,the term “hotspot” may be used in certain places synonymously with theterm “bad pattern.”

To illustrate embodiments of the invention and their advantages, thisdocument will illustrate its teachings particularly with respect torouters. It is noted, however, that the invention may be applied inother contexts as well and will be applicable to other classes ofdesign/verification/optimization tools.

FIG. 2 shows a general approach for implementing some embodiments of theinvention. The top portion of this figure describes a process forgenerating a fast model that can be used to identify hotspots and/or toroute a circuit design. The bottom portion of the figure describes aprocess for routing a clean design that would be free of known hotspots,either in its entirety or to a substantial extent.

The model building portion of the approach begins by receiving arepresentative layout 202 for analysis. In some embodiments, therepresentative layout 202 can be, for example, test chips that arecreated parametrically, created by the router, chosen by designers, orselected by other means. The representative layout 202 is meant to berepresentative of actual design to be routed at a later point.

The user's golden flow 204 is then used to perform model building 206 tocreate a library/database 208 of fast models. Model building for fastmodels can be performed by executing layout optimization and simulationupon the representative layout 202, such as by performing lithographysimulation using the customer's expected manufacturing facility andprocess parameters to predict the as-manufactured result based upon therepresentative layout 202. The user's RET tools and configurations mayalso be used to optimize and predict the manufactured result of thelayout configuration. The term RET (reticle enhancement technology), asused here, includes the use of, for example, optical proximitycorrection (OPC), sub-resolution assist features (SRAF), phase shiftmasks (PSM), etc. The results of the simulation 204 are used to identifypatterns within the design. The bad patterns would correspond to areasof the design that are likely to result in yield or functionalityproblems that are induced by the manufacturing process. Fast models inthe form of fast pattern models can be maintained to store informationabout the identified patterns.

According to some embodiments, the fast model, e.g., the fast patternmodel, comprises a search structure corresponding to patterns, where apattern is presented to the fast model and a result is retrieved. Thistype of search can be exact or inexact. A simple exact fast model can becreated using a hash table. A tree (or prefix tree) is an ordered treedata structure that can be used for fast exact and inexact search. Aneural net can also be trained to do a fast inexact search and canpredict values not previously seen.

A library or database 208 of fast models is then built based on thesepatterns. The library/database 208 provides a cache of known simulationresults and identifies whether or not certain patterns are hotspots. Insome embodiments, only information about hotspots/bad patterns ismaintained in the library/database 208. In an alternate embodiment, bothknown good patterns as well as known bad patterns are maintained in thelibrary/database 208. The advantage of a database that only includes badpatterns is that it is likely to be a smaller database, which istherefore faster to search and less of a drain upon system resources. Insome embodiments, the library/database 208 can interpolate from amongthe known hotspots to predict variations of known hotspots. In someembodiments, only known good patterns are kept in the library/database208.

According to one specific embodiment, the library/database 208implements the fast models in the form of stored patterns. The patternsstored in the library/database 208 correspond to particular combinationsof shapes and patterns that appear in the layout. The patterns may alsobe associated with an indication of the whether the pattern is a goodpattern or a bad pattern.

As noted above, the bottom portion of FIG. 2 describes a process forrouting a clean design to be free of known hotspots. A pre-route design214 is received to undergo the process. Routing is performed at 212.Pattern matching 210 is performed to quickly look up known andinterpolated hotspots from the library/database 208 of hotspots. Onesuitable approach that can be taken to perform pattern matching isdescribed in co-pending U.S. patent application Ser. No. 11/609,901,filed on Dec. 12, 2006, and entitled “Fast Pattern Matching”, which ishereby incorporated by reference in its entirety.

In practice, this process operates by having the router identify one ormore proposed routing configurations for portions of the layout. Patternmatching is performed to determine whether the proposed routingconfiguration matches a known bad pattern from the database 208. If thepattern-matching identifies a hotspot within the proposed routingconfiguration, then that problematic hotspot can be corrected bymodifying the proposed routing configuration to an alternate routingconfiguration that does not match up to a bad pattern. Alternatively,the router can simply select a known good pattern from the library toimplement a routing configuration. Once all hotspots have beenaddressed, the cleanly routed design 216 can be output for furtherprocessing for the next stage of the design process.

There are numerous advantages to this type of inventive approach. Forexample, this approach is much faster, since pattern matching can be amuch faster operation than actually performing simulation. Moreover,this approach provides a much more accurate result. This is becauseother non-simulation approaches use various shortcuts to compute aresult. These shortcuts can be characterized as performing an inaccuratesimulation, including an approximation of the effects of RET/OPC. Theshortcut non-simulation approach is inaccurate because, for example, itis not thorough enough with regards to non-lithography effects. Thepresent embodiment caches known simulation results using a goldenRET/OPC and simulation process to provide results that are at or almostas accurate as performing the simulation on the design itself.

In addition, the proposed solution can be directly integrated into therouting tool. Alternate approaches involve a loose combination of toolswhere the equivalent step of returning hotspot scores is done via anexternal tool. The present embodiment can be configured to actuallyintegrate pattern checking into the inner loop of the router itself sothat the output of the routing step is a layout that is hotspot clean.

Moreover, the present embodiment can be configured to natively operatewith 2-dimensional (2D) patterns. Bad 2-dimensional patterns aretypically very difficult to describe using scripts or by writing code.This embodiment can effectively handle 2-dimensional patterns nativelyas a 2-dimensional representation.

While the previous description has focused on a routing application, thesame approach can be used for any design-side layout verification. Forexample, the inventive approach can be used to speed up any current flowthat involves hotspot detection.

One example application is for full-chip hotspot detection, asillustrated in FIG. 3. The top portion of this figure describes aprocess for model building, which can use the same actions andmechanisms described with respect to FIG. 2, in which a representativelayout 302 is received for analysis. The user's golden flow 304 is thenused to perform model building 306 to create a library/database 308 offast models. The results of the simulation 304 are used to identifypatterns within the design. The bad patterns would correspond to areasof the design that are likely to result in yield or functionalityproblems that are induced by the manufacturing process. A library ordatabase 308 of fast models is then built based on these patterns. Thelibrary/database provides a cache of known simulation results andidentifies whether or not certain patterns are hotspots. Thelibrary/database 308 may implement the fast models in the form of storedpatterns. The patterns stored in the library/database 308 correspond toparticular combinations of shapes and patterns that appear in thelayout. The patterns may also be associated with an indication of thewhether the pattern is a good pattern or a bad pattern.

Unlike FIG. 2, the approach of FIG. 3 does not necessarily employ atight integration into a router to perform fast incremental checks onthe configurations that the router is processing. Instead, the layoutinput 314 can be any or all of the chip design, including a full-chipcheck, which is used for any portions of the design flow. Therefore, theinput 314 to the pattern matching action 310 can be an entire layout, orcould be a smaller portion of the full chip.

Pattern matching 310 is performed to determine whether the layoutportion under examination matches a known bad pattern from the database308. If the pattern-matching identifies a match to a known bad pattern,then a determination is made of the existence of a hotspot within theproposed layout. The output 318 of this process, according to someembodiments, is the location and/or severity of hotspots that have beenfound in the layout.

FIG. 4 shows an example full or partial chip hotspot analysisapplication flow according to some embodiments of the invention. In thisflow, a layout design 402 is received for analysis.

The design 402 undergoes situation extraction at 404. The goal ofsituation extraction, also referred to as pattern extraction, is toreduce the layout into a set of patterns that has a reasonably goodcoverage over the design. According to one approach, this actiondecomposes the layout into the geometric primitives that exist in thedesign, e.g., the basis or unique patterns in the layout. The choicesassociated with this decomposition (the situation semantics) fall into,for example, three areas: anchor, radius, and canonicalization. Adifferent set of choices will result in a different choice of basispatterns.

Suitable potential anchors include, for example: (a) layout corners; (b)simulation cutlines; (c) subset of the simulation cutlines, such as endsof the cutlines, middle of the cutlines, etc; (d) along edges; (e) alongthe center line of polygons; and/or (f) everywhere.

Suitable choices of radius include, for example: (a) at the opticalradius (e.g., about 1.2 um); (b) something smaller to reduce the datavolume; and/or (c) at multiple radii—for instance, a simple two radiioption might use the optical radius (for best coverage) and a smallerradii. The data within the smaller radii might be preserved in greaterdetail than the data between the smaller radii and the optical radius(for which some very lossy transformation may be applied).

Canonicalization is the process of converting data that has more thanone possible representation into a “standard” canonical representation;for instance, patterns that are equivalent except for a 90 degreerotation can be folded together into one canonical representation. Somesuitable choices of canonicalization include, for example: (a)rotational; (b) mirror; (c) translational; and/or (d) lossy transform.An example of a suitable lossy transform is a Gaussian filter. If thesame filter is applied to all patterns, patterns that are identicalafter this filter are folded together. In some embodiments, a filter isapplied that approximates the behavior of the lithography system.

One suitable approach that can be taken to perform situation extractionis described in co-pending U.S. patent application Ser. No. 11/207,267,filed on Aug. 18, 2005, and entitled “System and Method for Analysis andTransformation of Layouts Using Situations”, which is herebyincorporated by reference in its entirety.

Multiple situations may overlap based upon the configuration of shapeswithin the situation. This is because the situation extraction mayproceed to a large number of locations, e.g., corners in the design,where adjacent corners may give rise to adjacent situations that shareregions that are the same or similar. According to some embodiments,these same or similar regions can be collapsed into the same entrywithin a pattern database. These overlapping regions can then besimulated as a group, rather than separate simulations for each pattern.

A layout index 406 can be maintained for the identified patterns fromthe design(s). In some embodiments, the layout index 406 provides analternate representation of the designs that include the extractedsituations plus information about where the situations are instancedwithin the design.

The situations within the layout index 406 will then undergo patternmatching 410 against a database 408 of bad patterns. If thepattern-matching 410 identifies a match to a known bad pattern, then adetermination is made of the existence of a hotspot within the design.The output 412 of the process is information about the identifiedhotspots, including for example, the location and/or severity of theidentified hotspots.

As noted above, some embodiments of the invention can maintain adatabase 416 of good patterns in addition to, or instead of, thedatabase 408 of bad patterns. The same or similar flows can be used tostore patterns that have been validated as clean. The pattern matchingprocess can be used to retrieve only good patterns that a router orother design tool is permitted to employ.

In the present flow, an index comparison action 414 is used to identifywhether any of the extracted situations correspond to a known goodpattern from the good pattern database 416. In some embodiments, thegood pattern database 416 is maintained using a hashing function thathashes the good pattern to an entry within the database 416. The indexcomparison action 414 would then be implemented by hashing the situationunder examination to the database to check for a match. If the hashfunction is appropriately defined, and collisions are minimized, then insome embodiments, the geometric patterns themselves do not need to bemaintained. Instead, only the hash signature is maintained in thedatabase 416. To optimize memory usage and efficiencies, the knownpattern library 416 could be split onto multiple nodes or processingmachines.

Any situations that have not been identified by this point could beconsidered “unscored” situations 418. These would be patterns andconfigurations for which it is unknown whether they represent good orbad patterns. These situations would undergo simulation and REToptimization 420 to determine whether or not any would be consideredhotspots. The output is an incremental hotspot analysis 422 of thedesign.

In some embodiments, the situations from 418 could be re-tiled back intothe original layout location to reduce the total area that is needed,e.g., to reduce resource consumption of the described process. Asdescribed earlier, situations may overlap and it may be advantageous toperform the described process on a group of situations that sharegeometry as a single region rather than individually on each situation.

FIG. 5 shows a possible implementation of a model building flowaccording to some embodiments of the invention. The purpose of this flowis to generate a database of patterns that can be used for patternchecking. The flow of FIG. 5 begins by receiving a representative layout502 for processing. In some embodiments, the representative layouts 502comprise known designs that can be run through a user's goldensimulation/optimization flow 504.

The output from the simulation/optimization action 504 is a set ofscores 530 for patterns within the layout. A score represents aquantitative or qualitative value that represents the degree ofmanufacturability or usability associated with the pattern. Instead ofjust having a “yes” or “no” value associated with the existence of ahotspot, the score value can be used to identify the degree of severityof a given pattern or hotspot.

For the present flow, all scores 530 for the patterns are provided fromthe golden flow 504—regardless of whether they relate to “good” patternsor “bad” patterns. This means that the libraries that will be generatedfrom this process will include patterns that both correspond to hotspotsas well as patterns that are not associated with hotspots. Other flowswill be discussed below, such as the flow of FIG. 7, which can be usedto create libraries that contain only hotspot scores. Other flows maycontain only “good” patterns.

Cutline scores 506 would be generated for cutlines within the layout. Acutline corresponds to a measurement location/point within the layout.The cutline score would be scores that correspond directly to thecutline locations. In alternate embodiments, the scores are based uponnon-cutline locations, such as points, edges, rectangles, or markers.

As described below, a set of patterns or situations is generated foranalysis. The patterns are used in combination with the cutline scores506 to generate a library 516 of “inexact” patterns. One possibleapproach is to build a neural net 514 to generate the library 516 usingconventional neural net techniques known to those skilled in the art.The library 516 includes scores for the patterns represented in thelibrary.

The representative layouts 502 could also be used in a re-targetingaction 508. Re-targeting is employed in the present embodiment togenerate a set of patterns that forms an exact match library 522 ofhotspot patterns. Unlike the inexact library 516, the exact matchlibrary 522 definitively states whether a given pattern corresponds toan entry in the library 522. In contrast, the inexact library 516 hasentries that may correspond to multiple patterns, e.g., based upon aneural net algorithm.

The re-targeting action 508 can also be used in some sense to “mimic” orrecreate the results of the user's golden flow 504 by attempting toreplicate the processing and simulation results from the golden flow504. This is particularly useful if the golden flow 504 is implementedby a tool whose internal operation is unknown or confidential, e.g., atool that is provided by a first tool vendor different from a secondtool vendor providing the tool that is performing the re-targetingaction. In some embodiments, the actual retargeted layout is availableas an output from the golden flow 504 and is used directly instead ofperforming the action 508.

The output from the re-targeting action 508 undergoes pattern extraction510 to extract a set of patterns or situations from the re-targetedlayout. As noted above, the goal of pattern/situation extraction is todecompose the layout into the geometric primitives that exist in thedesign, e.g., the basis or unique patterns in the layout. The choicesassociated with this decomposition (the situation semantics) fall into,for example, three areas: anchor/corner, radius, and canonicalization. Adifferent set of choices will result in a different choice of basispatterns. Suitable potential anchors include, for example, cutlinelocations. Suitable choices of radius include, for example, a radius upto the optical radius. Canonicalization is the process of convertingdata that has more than one possible representation into a “standard”canonical representation; for instance, patterns that are equivalentexcept for a 90 degree rotation can be folded together into onecanonical representation. Some useful choices of canonicalizationinclude, for example, rotational or mirroring.

The results of pattern extraction could very well include an excessiveamount of information about the patterns in the layout. If so, then itmay be appropriate to apply some sort of filtering to reduce the volumeof pattern data to be processed from the layout. According to someembodiments of the invention, to reduce the volume of data and toimprove the grouping of “similar” patterns, a lossy transform 512/518can be applied to the patterns. Some choices of actions to perform thelossy transform, include for example: (a) radon transforms; (b) Gaussiantransforms; (c) weighted moments; and/or (d) principal componentsanalysis.

The scores 530/506 and the extracted patterns are used to generate thefast model library 522. According to some embodiments, the fast modelcomprises a search structure corresponding to patterns, where a patternis presented to the fast model and a result is retrieved. An exact fastmodel can be created using a hash table where a hash function isemployed to hash to specific entries based upon the score and/orpattern. In some embodiments, only the hash signatures are maintainedand the actual geometries are discarded. In an alternate embodiment, thehash signatures and the geometries are both maintained in the library.The hash can also be used to return a score for the marker.

FIG. 6 shows a pattern matching flow according to one embodiment of theinvention, e.g., to utilize the models generated by the process of FIG.5. A given pattern 602 from the design under examination is received asinput to this process. At 604, the pattern 602 is compared against theexact library 522 to determine if an exact match is found against thepatterns in the library 522. If the pattern 602 is found in the library522, then the score for that pattern is returned at 608.

If the pattern 602 is not found in the exact library 522, then the nextstep is to determine if a match can be found in the inexact library 516.Lossy reduction 612 is performed to determine whether multiple patternsmay correspond to the same category or entry within the inexact library516. As noted above, there are numerous loss reduction techniques thatcould be employed, including for example: (a) radon transforms; (b)Gaussian filtering; (c) weighted moments; and/or (d) principalcomponents analysis. In some embodiments, some or all of the lossyreductions actions 512, 518, and 612 are the same.

A determination is made at 614 whether a close match can be found to thepattern 602 within the inexact library 516. If a close enough match isfound, 616, then at 618, the score for the pattern entry in the inexactlibrary 516 for the close match is returned as the score for thepattern.

If no patterns within the inexact library 516 are close enough, then thegolden flow 622 of the user's simulation/optimization actions isperformed to generate a new score 624 for the pattern 602. A feedbackloop can be implemented to update the libraries 516 and 522 with thescore 624 for the pattern 602.

It is noted that while Phase I, Phase II, and Phase III are describedsequentially, these phases can occur in parallel in various embodimentsof the invention.

FIG. 7 shows another possible implementation of a model building flowaccording to some embodiments of the invention. Unlike the flow of FIG.5, model building flow of FIG. 7 will create one or more libraries thatseparate out the bad patterns from the other patterns. The flow of FIG.7 begins by receiving a representative layout 702 for processing. Insome embodiments, the representative layouts 702 comprise known designsthat can be run through a user's golden simulation/optimization flow704.

Along a first path, the output from the simulation/optimization action704 is a set of scores 724 that correspond only to hotspots within therepresentative layouts 702. Along a second path, the output from thesimulation/optimization action 704 is a set of all scores 730 for fromthe layout, regardless of whether or not they correspond to a hotspot.Cutline scores 706 are generated for cutlines within the layout basedupon the scores 730.

A set of patterns/situations is generated for analysis usingpattern/situation extraction 710 a. The choices associated with thisdecomposition (the situation semantics) fall into, for example, threeareas: anchor/corner, radius, and canonicalization. A different set ofchoices will result in a different choice of basis patterns. Suitablepotential anchors include, for example, any deterministic approaches ortechniques. Suitable choices of radius include, for example, anything upto an optical radius. Suitable choices for canonicalization include, forexample, pattern signature vector transformation, such as described inco-pending U.S. patent application Ser. No. 11/952,912 filed on Dec. 7,2007, which is hereby incorporated by reference in its entirety.

The patterns from pattern/situation extraction 710 a may undergo a lossyreduction 718 a. The results of the lossy reduction 718 a are employedto create a look-up table 720 a that is used to index the bad patternsin the bad pattern library 716. The bad pattern library 716 onlyincludes entries and scores for hotspots, e.g., as identified from therepresentative layout 702. In operation, a match against an entry in thebad pattern library 716 would retrieve the score associated with the badpattern.

The representative layouts 702 could also be used in a re-targetingaction 708 to create a library 722 of known patterns, including bothgood and bad patterns. As described above, the re-targeting action 708can be used to mimic or recreate the results of the user's golden flow704. The output from the re-targeting action 708 undergoes patternextraction 710 b to extract a set of patterns or situations from there-targeted layout. As noted above, the goal of pattern/situationextraction is to decompose the layout into the geometric primitives thatexist in the design, e.g., the basis or unique patterns in the layout.The choices associated with this decomposition (the situation semantics)fall into, for example, three areas: anchor/corner, radius, andcanonicalization. A different set of choices will result in a differentchoice of basis patterns. Suitable potential anchors include, forexample, anchoring based upon edges. Suitable choices of radius include,for example, the optical radius or some fraction of the optical radius.Suitable choices for canonicalization include, for example, a lossytransformation where a central core region at a small radius is storedexactly, but a Gaussian filter is applied at a radius outside of thecore region. In some embodiments, the actual retargeted layout isavailable as an output from the golden flow 704 and is used directlyinstead of performing the action 708.

The patterns from pattern/situation extraction 710 b may undergo a lossyreduction 718 b. The results of the lossy reduction 718 b are employedto create a look-up table 720 b that is used to index the known patternsin the known pattern library 722. The known pattern library 722 includesentries and scores for the all known patterns, e.g., as identified fromthe representative layout 702. In operation, a match against an entry inthe known pattern library 722 would retrieve the score associated withthe identified pattern.

FIG. 8 shows a pattern matching flow according to one embodiment of theinvention, e.g., to utilize the models generated by the process of FIG.7. A given pattern 802 from the design under examination is received asinput to this process. Lossy reduction 812 is performed to determinewhether multiple patterns may correspond to the same category or entrywithin the bad pattern library 716. A determination is made at 814whether a close match can be found to the pattern 802 within the entriesin the bad pattern library 716. If a close enough match is found, 816,then at 818, the score for the pattern entry in the bad pattern library716 for the close match is returned as the score for the pattern.

At 804, the pattern 802 is compared against the entries in the knownpattern library 722 to determine if an exact match is found against thepatterns in the known pattern library 722. If the pattern 802 is foundin the library 722, then the score for that pattern is returned at 808.

Alternatively and/or if no match is found within the known patternlibrary 722, the golden flow 822 of the user's simulation/optimizationactions is performed to generate a new score 824 for the pattern 802. Afeedback loop can be implemented to update the libraries 716 and 722with the score 824 for the pattern 802.

It is noted that while three separate processing paths are described inFIG. 8, these processing paths can occur either sequentially or inparallel.

FIG. 9 shows yet another possible implementation of a model buildingflow according to some embodiments of the invention. Unlike the previousflows, the model building flow of FIG. 9 will create separate librariesfor both the good patterns and the bad patterns. The flow of FIG. 9begins by receiving a representative layout 902 for processing. In someembodiments, the representative layouts 902 comprise known designs thatcan be run through a user's golden simulation/optimization flow 904. Theoutput from the simulation/optimization action 904 is a set of scores924 that correspond only to hotspots within the representative layouts902.

A set of patterns/situations is generated for analysis usingpattern/situation extraction 910 a. The choices associated with thisdecomposition (the situation semantics) fall into, for example, threeareas: anchor/corner, radius, and canonicalization. A different set ofchoices will result in a different choice of basis patterns. Suitablepotential anchors include, for example, edges in the design. Suitablechoices of radius include, for example, an optical radius or somefraction of the optical radius. Suitable choices for canonicalizationinclude, for example, translational uniquing techniques where patternsthat are identical except under translation are folded together.

The patterns from pattern/situation extraction 910 a may undergo a lossyreduction 918. The results of the lossy reduction 918 are employed tocreate a look-up table 920 that is used to index the bad patterns in thebad pattern library 916. The bad pattern library 916 includes entriesand scores for hotspots, e.g., as identified from the representativelayout 902, the golden flow 904, the hotspot scores 924, or the look-uptable 920.

A set of patterns/situations is generated for analysis usingpattern/situation extraction 910 b to create a library 922 of known goodpatterns. The choices associated with this decomposition (the situationsemantics) fall into, for example, three areas: anchor/corner, radius,and canonicalization. A different set of choices will result in adifferent choice of basis patterns. Suitable potential anchors include,for example, rectangles in the design. Suitable choices of radiusinclude, for example, an optical radius or some fraction of the opticalradius.

The output of the golden flow 904 can be used to filter out 940 the badpatterns, leaving only known good patterns to be stored within the knowngood pattern library 922.

FIG. 10 shows a pattern matching flow according to one embodiment of theinvention, e.g., to utilize the models generated by the process of FIG.9. A given pattern 1002 from the design under examination is received asinput to this process. At 1004, the pattern 1002 is compared against theentries in the known good pattern library 922 to determine if an exactmatch is found against the known good patterns in the good patternlibrary 922. If the pattern 1002 is found in the library 922, it isknown that the pattern is not a hotspot, and a result 1008 to thateffect is issued for display on a display device or stored in a computerreadable medium.

Lossy reduction 1012 is performed to determine whether multiple patternsmay correspond to the same category or entry within the bad patternlibrary 916. A determination is made at 1014 whether a close match canbe found to the pattern 1002 within the entries in the bad patternlibrary 916. If a close enough match is found, 1016, then at 1018, thescore for the pattern entry in the bad pattern library 916 for the closematch is returned as the score for the pattern.

Alternatively and/or if no match is found within the known good patternlibrary 922 or the bad pattern library 916, then the golden flow 1022 ofthe user's simulation/optimization actions is performed to generate anew score 1024 for the pattern 1002. A feedback loop can be implementedto update the libraries 916 and 922 with the score 1024 for the pattern1002. It is noted that while three separate processing paths aredescribed in FIG. 10, these processing paths can occur eithersequentially or in parallel.

FIG. 11 shows an improved routing flow that takes advantage of patternmatching to optimize designs for routing. This flow is a variation ofthe flow described in FIG. 2 where the fast model is a library/databaseof bad patterns. In this flow, the representative layouts 1102 are used,in addition to simulation and optimizations activities 1104, to performautomated hotspot detection 1106 as described above to generate a badpattern library 1108. An un-routed design 1110 would undergo patternmatching 1112 during the routing process 1114.

Based upon the pattern checking 1112, any immediate recognition ofhotspots in the proposed routing can be corrected. As only hotspots thathave previously been encountered are flagged with this flow, additionalsimulation and optimization (e.g., RET) 1116 can be performed to verifythat there are no new hotspots and to handle new hotspots if they exist.The output of this simulation/optimization action 1116 is a routeddesign that has its hotspots corrected 1118, e.g., in a lithographicallycorrect manner, by using manufacturing aware processes such as RET orOPC.

A feedback loop can be implemented for this process to improve thequality of the bad pattern library 1108. Bad patterns can be added tothe bad pattern library 1108 from the back-end simulation/optimizationactions when they are identified.

In other embodiments, the flow in FIG. 11 uses a good pattern libraryinstead of the bad pattern library 1108.

FIG. 12 shows an improved routing flow that takes advantage of patternmatching to optimize designs for routing and to improve the quality ofrules used for DRC operations. In this flow, the representative layouts1202 are used with simulation and optimizations activities 1204 toperform automated hotspot detection 1206 to generate a bad patternlibrary 1208. An un-routed design 1210 would undergo pattern matching1212 during the routing process 1214. The bad pattern library 1208 isused as described in FIG. 11.

In addition, additional hotspot analysis 1220 would be performed todetermine whether any of the hotspots are such that they deserveinclusion as new 2D DRC rules within DRC ruledecks 1222. In someembodiments this action is performed as a user-based analysis ratherthan automated analysis. Alternatively, this action can be automated aswell, e.g., based upon heuristic rules. In some approaches, the badpatterns are upgraded to the new DRC rules only if they occur frequentlyor are critical in nature. It is contemplated that there will be greaternumbers of bad patterns in the bad pattern library 1208 as compared tothe number of new DRC rules within the new DRC library 1222. In someembodiments, the new 2D DRC rules can include a representation of thepattern itself that can be used by a pattern matcher.

Based upon the 2D DRC checking 1212, any immediately recognition of DRCviolations from the new 2D DRC library 1222 can be identified. The knownhotspots that have been chosen to be included as rules within the newDRC library 1222 can therefore be identified and corrected. Any furtherhotspots that have not been included as rules can be identified andcorrected by performing additional simulation and optimization (e.g.,RET) 1216 upon the routed design. The output of thissimulation/optimization action 1216 is a routed design that has itshotspots corrected 1218, e.g., in a lithographically correct manner, byusing manufacturing aware processes such as RET or OPC. In someembodiments, the 2D DRC checking 1212 can include a pattern matchingstep to quickly find the bad patterns that have been included as new 2DDRC rules in the DRC ruledeck 1222.

A feedback loop can be implemented for this process to improve thequality of the bad pattern library 1208 and to determine whether anyadditional DRC rules should be implemented in the DRC library 1222.

System Architecture Overview

FIG. 13 is a block diagram of an illustrative computing system 1400suitable for implementing an embodiment of the present invention.Computer system 1400 includes a bus 1406 or other communicationmechanism for communicating information, which interconnects subsystemsand devices, such as processor 1407, system memory 1408 (e.g., RAM),static storage device 1409 (e.g., ROM), disk drive 1410 (e.g., magneticor optical), communication interface 1414 (e.g., modem or Ethernetcard), display 1411 (e.g., CRT or LCD), input device 1412 (e.g.,keyboard), and cursor control.

According to one embodiment of the invention, computer system 1400performs specific operations by processor 1407 executing one or moresequences of one or more instructions contained in system memory 1408.Such instructions may be read into system memory 1408 from anothercomputer readable/usable medium, such as static storage device 1409 ordisk drive 1410. In alternative embodiments, hard-wired circuitry may beused in place of or in combination with software instructions toimplement the invention. Thus, embodiments of the invention are notlimited to any specific combination of hardware circuitry and/orsoftware. In one embodiment, the term “logic” shall mean any combinationof software or hardware that is used to implement all or part of theinvention.

The term “computer readable medium” or “computer usable medium” as usedherein refers to any medium that participates in providing instructionsto processor 1407 for execution. Such a medium may take many forms,including but not limited to, non-volatile media and volatile media.Non-volatile media includes, for example, optical or magnetic disks,such as disk drive 1410. Volatile media includes dynamic memory, such assystem memory 1408.

Common forms of computer readable media includes, for example, floppydisk, flexible disk, hard disk, magnetic tape, any other magneticmedium, CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, RAM, PROM, EPROM,FLASH-EPROM, any other memory chip or cartridge, or any other mediumfrom which a computer can read.

In an embodiment of the invention, execution of the sequences ofinstructions to practice the invention is performed by a single computersystem 1400. According to other embodiments of the invention, two ormore computer systems 1400 coupled by communication link 1415 (e.g.,LAN, PTSN, or wireless network) may perform the sequence of instructionsrequired to practice the invention in coordination with one another.

Computer system 1400 may transmit and receive messages, data, andinstructions, including program, i.e., application code, throughcommunication link 1415 and communication interface 1414. Receivedprogram code may be executed by processor 1407 as it is received, and/orstored in disk drive 1410, or other non-volatile storage for laterexecution.

In the foregoing specification, the invention has been described withreference to specific embodiments thereof. It will, however, be evidentthat various modifications and changes may be made thereto withoutdeparting from the broader spirit and scope of the invention. Forexample, the above-described process flows are described with referenceto a particular ordering of process actions. However, the ordering ofmany of the described process actions may be changed without affectingthe scope or operation of the invention. The specification and drawingsare, accordingly, to be regarded in an illustrative rather thanrestrictive sense.

1. A computer implemented method for building a fast model to implementrouting for an electronic design, comprising: performing manufacturingaware analysis upon a layout to identify patterns associated with thelayout that correspond to good or bad patterns, wherein the patternscorrespond to post-routing layout objects; generating one or morelibraries of the patterns, the one or more libraries to be used by arouter to route an electronic design; and storing the one or morelibraries into a non-transitory computer readable medium.
 2. Thecomputer implemented method of claim 1, in which the one or morelibraries comprise an exact library which comprises a set of patternsthat will be exactly matched during analysis of the electronic design.3. The computer implemented method of claim 1, in which a hash functionis employed to implement the exact library.
 4. The computer implementedmethod claim 1, in which the one or more libraries comprise an inexactlibrary which comprises a set of patterns that does not have to beexactly matched during analysis of an electronic design.
 5. The computerimplemented method of claim 4, in which a neural net is employed toimplement the inexact library.
 6. The computer implemented method claim1, in which the one or more libraries comprise a known good patternlibrary and a known bad pattern library.
 7. The computer implementedmethod of claim 1, in which retargeting is employed to generate data forpattern extraction.
 8. The computer implemented method of claim 1, inwhich a lossy reduction is employed to reduce data quantity for storagein the one or more libraries.
 9. The computer implemented method ofclaim 1, in which the manufacturing aware analysis is used to generate ascore for a portion of the layout.
 10. The computer implemented methodof claim 10, in which a set of scores is generated, wherein the set ofscores comprises scores only for hotspots, scores only for goodpatterns, or scores for all analyzed patterns.
 11. A system for routingan electronic design, comprising: at least one processor that is to:perform manufacturing aware analysis upon a layout to identify patternsassociated with the layout that correspond to good or bad patterns,wherein the patterns correspond to post-routing layout objects; generateone or more libraries of the patterns, the one or more libraries to beused by a router to route an electronic design; and store the one ormore libraries into a non-transitory computer readable medium.
 12. Thesystem of claim 11, in which the one or more libraries comprise an exactlibrary which comprises a set of patterns that will be exactly matchedduring analysis of the electronic design.
 13. The system of claim 11, inwhich the one or more libraries comprise an inexact library whichcomprises a set of patterns that does not have to be exactly matchedduring analysis of an electronic design.
 14. The system of claim 11, inwhich the at least one processor is further to: generate data forpattern extraction by using a retargeting technique.
 15. The system ofclaim 14, in which the at least one processor that is to generate databy using the retargeting technique is further to: mimic or recreate ofan existing design flow for replicate a processing result or asimulation result of the existing design flow.
 16. A computer programproduct comprising a non-transitory computer accessible storage mediumhaving stored thereupon a sequence of instructions which, when executedby at least one processor, causes the at least one processor to executea method for routing an electronic design, the method comprising: usingat least one processor to perform a process, the process comprising:performing manufacturing aware analysis upon a layout to identifypatterns associated with the layout that correspond to good or badpatterns, wherein the patterns correspond to post-routing layoutobjects; generating one or more libraries of the patterns, the one ormore libraries to be used by a router to route an electronic design; andstoring the one or more libraries into a non-transitory computerreadable medium.
 17. The computer program product of claim 16, in whichthe one or more libraries comprise an exact library which comprises aset of patterns that will be exactly matched during analysis of theelectronic design.
 18. The computer program product of claim 16, inwhich the one or more libraries comprise an inexact library whichcomprises a set of patterns that does not have to be exactly matchedduring analysis of an electronic design.
 19. The computer programproduct of claim 16, in which the process further comprises: generatingdata for pattern extraction by using a retargeting technique.
 20. Thecomputer program product of claim 19, in which the action of generatingthe data comprises: mimicking or recreating of an existing design flowfor replicate a processing result or a simulation result of the existingdesign flow.